high performance private cloud for satellite data …manjrasoft.com/satellitecloud2012.pdf · 2016....

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Copy Right 2012, This paper appeared in the Proceedings of the First International Conference on Advances in Cloud Computing (ACC-2012), July 26-28, 2012 Bangalore, India. Universities Press, Rajiv Ranjan, Raj Buyya and Anirban Basu(Editors)

HIGH PERFORMANCE PRIVATE CLOUD

FOR SATELLITE DATA

PROCESSING – ENGINEERING IN CLOUD Raghavendra Kune

1, Ankit Chaudhri

2, Pramod Kumar Konugurthi

3, Geeta Varadan

4

Advanced Data Processing Research Institute (ADRIN),

Department of Space, Government of India,

203 Akbar Road, Tarbund, ManovikasNagar (P.O), Secunderabad-9,

INDIA {1raghav,

3pramod}@adrin.res.in

[email protected]

[email protected]

Abstract— In this paper we present a private cloud for satellite

data processing and expect to see increased throughput,

significant cost reduction in the compute infrastructure, easy

management of the system and low maintenance over a

conventional multi core desktop satellite data processing system.

In Satellite data processing; “Data product generation” is an

engineering activity, often used to process the satellite data and

produce the data products, called “value added data products”,

such data products are good quality images improved in

radiometry and geometry over the raw/unprocessed data. These,

data products are often used in analysing the earth observation

systems such as object detection, object change analysis, urban

image mapping etc. In Remote Sensing field, the satellite images

are acquired at the ground stations from the various satellite

sensors in the earth orbit; and are archived at Data Centres for

data product generation and application specific analysis. Here,

we discuss one such existing satellite data processing systems

based on MPI implementation for distributed processing on

multi core desktop systems in a data centre and systematically

analyses the systems limitations such as dynamic scaling of the

computational resources, limitations in MPI (Message Passing

Interface) based data product processing, low utilization of the

physical compute resources and show the experimental results

over processing on physical Vs virtual systems. Also, here we

illustrate the technologies used in the private cloud for satellite

data processing.

Keywords— Cloud Computing; Virtualization; MPI; Data

Products; Aneka Programming Model; Data Grid; OGC

Services; Engineering in Cloud.

1. Introduction

Remote Sensing involves various ground stations setup across

the globe to acquire the images from the satellite sensors in

the earth orbit. Due to the visibility limitations of the satellite

imaging, the ground stations are geographically distributed.

However, such received images are at the ground stations are

accumulated in the data storage areas at respective reception

centers constitute the data grid[1].The information extracted

from these remotely sensed images is being applied for

various activities like weather forecasting and climate science,

cartography and applications like agriculture, town planning,

natural resource management, disaster management etc[2].

The complexity involved in data analysis and modeling these

data sets has been augmented over the last four decades due to

the continuous growth in the capabilities of instrumentation of

remote sensing satellites. The demand for various data

products (in the form of value additions) generated from

remotely sensed data is also growing due to increase in the

number of user groups. In order to meet these demands, more

satellites are placed in the orbit, which makes processing,

archiving, and retrieval of data, a complex activity. Satellite

images are often large in data size and current remote sensing

systems generate hundreds of gigabytes of raw sensor data per

day to produce useful data products. The process involved in

the generation of these products is time consuming and

demands large computational and infrastructural resources.

Cloud computing has emerged as a next generation computing

in IT and uses service oriented architectures for delivering

resources such as compute, storage and software as services.

Cloud computing facilitates in reducing the infrastructure cost

and effective utilization of the resources. Cloud infrastructure

can be classified as public, private, or hybrid based on the

model of deployment [3] and services offered to its’ users. In

a public cloud 3rd

party manages cloud infrastructure and

services which are available on subscription basis (pay as you

go model). In a private cloud deployment a

company/organization manages its own cloud infrastructure

for internal and/or partners use. A hybrid cloud takes shape

when a private cloud is supplemented with computing

capacity from public clouds [4]. The clouds offer services at

IaaS (Infrastructure as a Service), PaaS (Platform as a service)

and SaaS (Software as a Service) layers. Offering virtualized

resources (computation, storage and communication) on

demand is known as Infrastructure as a Service. PaaS offers a

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higher level of abstraction to make a cloud easily

programmable. A cloud platform offers an environment on

which developers create and deploy applications and do not

necessarily need to know the number of processors or amount

of memory required for using the application. Software as a

Service (SaaS) allows users to access applications via web

services, thus eliminating the need to install applications on

their local systems and other maintenance issues.

In this paper we described a model to transform the existing

data center satellite data processing system to a private cloud,

enabling infrastructure virtualization, resource provisioning

and applications as services. The reasons for moving to the

cloud are; easy management of compute and storage

infrastructure, hiding data and process complexities, better

utilization of the resources and achieving high throughput and

higher return on investment.

The paper is organized into four sections. Section 2 describes

the related work, section 3&4 depicts satellite data processing

in the traditional existing model, section 5 describes design

and architecture of satellite data processing in private cloud

and section 6 depicts the experimental results.

2. Related Work

Remote Sensing data processing involves processing the large

data sets to deliver the good accuracy data products. In the

recent past works are conducted to process the data using the

technologies; cluster, grid and recently cloud. Christopher et

al. [5] presented their work on facilitating NASA earth science

Data processing using Nebula Cloud Computing and

OpenStack[6]. An eScience application Data Re-projection

and reduced pipeline on MODIS satellite data over Windows

azure [7] demonstrated satellite data processing with a goal of

hiding data complexities from end users. Environmental data

processing and products derivation of NOAA satellite and

information service to domestic and foreign users is

demonstrated at OSDPD [8]. Ammar et al. [9] discussed the

basic issues in data usage and processing on cloud and their

limitations over public clouds. Karmen et al. [10] addressed a

satellite based cloud computing and virtualized information

resources and the impact of satellite cloud computing on the

development of satellite information clouds as open access

public resources. Natavia et al. [11] presented their experience

with in the development of the Grid Systems for satellite data

processing in the Space Research Institute of NASA-NSAU

and the conceptual foundations for the development of grid

systems that aimed for satellite data processing. The

applicability of Cloud Computing to a large-scale satellite

ground systems and architectures that illustrate how several

Cloud Computing implementation approaches apply to ground

systems and aspects of satellite ground system architectures

are presented [12]. Golpayegahi et al. [13], presented a work

on processing a multiyear remote sensing data using Hadoop

Distributed File System and MapReduce Parallel Computing

Framework over the cloud of high end computer clusters.

Other works conducted on Satellite product generation such as

Service Oriented Utility Grid for Remote Sensing

Applications [14] for data product generation, and the 3-

Dimensional Visualization for the satellite images [15] are

works based on Rigorous sensor Model [16], are ideally suited

for Computational Grids.

Clouds are the next generation computing in IT for guaranteed

service delivery. Amazon Elastic Compute Cloud(Amazon

EC2)[17] is a public cloud ; offers web-scale computing and

storage environment with a variety of operating systems

running on Amazon’s computing infrastructure and on

Amazon Simple Storage Server Amazon S3[18]; this

infrastructure is used for public domain, still a challenge for

best data security and large scale data processing. Windows

Azure [19] offering on-demand compute, storage, scale and

manage web applications on the internet through Microsoft

data centers. Microsoft Hyper-V [20] on Microsoft Windows

Server 2008 offers windows based virtual machines. Ubuntu

Enterprise cloud [21] is used to build private cloud using

KVM as the virtualization Technology and Walrus for

storage. The machine instances are compatible to Amazon

EC2 AMI and storage is compatible to Amazon S3. Ubuntu

Enterprise cloud is a choice for the enterprises to build private

cloud which are compatible to Amazon EC2 public cloud.

Eucalyptus [22] is cloud software for enterprises and

government agencies to build and own private/hybrid cloud

computing environment. Aneka [23] is the platform (PaaS) for

design and deployment of .NET based services on

private/public/hybrid clouds. Aneka offers programming

models to build applications as services for the cloud,

enabling efficient use of the computing infrastructure with

dynamic resource provisioning and scheduling mechanisms.

One such private cloud for satellite data product generation

was demonstrated in our earlier work presented at the

CCGRID 2010[24] using Aneka task based programming

model. Aneka uses resource pools configured with various

hypervisors. Xen[25] and Vmware [26] hypervisors configure

dynamically scalable virtual machines running on the bare

minimum hardware.

Combination of open source softwares such as GeoServer[27]

etc, and OpenLayers address presentation of remote sensing

data on a web client using OGC’s WMS and WFS standards.

The current work describes a private cloud framework for

Remote Sensing applications to derive the optimal resources

for satellite product generation.

3. Satellite Data Processing

Satellite data processing consists of various methods to correct

the radiometric errors and geometric distortions in the basic

data generated by the sensor; this data is termed as Level-0.

The procedures like georeferencing and registration [28]

applied on the Level-0 data to generate the products such as;

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Level 1 – Radio metrically corrected and

geometrically corrected only for earth rotation

(Browse product)

Level 2 – Both radiometric and geometrically

corrected (Standard product)

Level-0 data under goes through a Rigorous sensor Model

[16] (A series of transformation to convert a pixel to ground

coordinates) shown in Fig. 1, this process is termed as data

product generation, such data product is either systematically

or geometrically corrected. The generation process is a

compute intensive task based on scale (map) or coverage

(Region of interest) and here each task is independent and

considered to be a BOT (Bag of Tasks) for processing.

Fig. 1 Precision product generation using RSM

4. Existing Data Product generation System - Data

Center Model

Currently the data products are generated on the physical

desktops and generation process is tightly coupled with the

interactive elements and computing elements. The computing

process is based on MPI (Message Passing Interface) [29];

executes the jobs on the available physical desktop systems; in

the sequence one after another on the first come first serve

basis. The major drawback of the current system is lack of

features such as load balancing, failed jobs detection and re-

scheduling of the failure jobs, which are very important to

achieve higher throughput and reliability. Another important

limitation of the existing system is lower resource utilization

i.e., even though the jobs need less resources for computation,

entire system resources are locked. The existing system does

not scale the computing resources when the demand increases

for processing the more data products. To overcome the

issues, in the next section, we present a private cloud model

for dynamic scaling of the infrastructure, job execution in

parallel with load balancing, job monitoring and failure

request handling.

The paper addresses issues such as transformation from high-

end data center to the private cloud setup, transforming the

existing data product generation utility/application as the

software service, effective scheduling of the jobs, and

dynamic expansion of the computing resources for the

optimized resources utilization for remote sensing data

product generation.

5. High Performance Satellite Data Processing - Private

Cloud

The architecture of the proposed private cloud is shown in

Fig. 2. It is a three layered architecture; bottom layer forming

the infrastructure layer (IaaS); middle layer is the

development layer (PaaS) for application development and

dynamic resource provisioning, top layer is the application

layer (SaaS) where applications are deployed as services for

the end users. Platform layer is interfaced with the Data

Grid/Data Centre(s) layer to serve Data as a Service (DaaS) in

the proposed cloud. Data in remote sensing domain is

expensive and often the user ends up paying for the data even

though he/she does not use or require, since minimum extent

changes from vendor to vendor. Hence here we introduce

DaaS model, where flexibility can be provided to the users to

pick and choose between the sensors based on many

parameters such as cost, spatial resolution, temporal resolution

(when the image was acquired etc). However in the present

work only one cost model is used for DaaS. IaaS layer can be

configured with hypervisors to provide a scalable environment

for storage, compute and network.

Fig. 2 Architecture of the private cloud

Identify

GCPs

Construct &

Refine RSM

Select ROI &

DEM

Generate grid for given

DEM resolution for

given output space.

Do Resampling

Extract DEM and

compute output extents

based on Projection,

Datum and ROI.

Write into O/P

image.

For each cell

in DEM apply

Model to

obtain O/P

image pixel.

Input Image

& Ephemeris

Reference

Image Map

Digital Elevation

Model

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PaaS layer is configured with Aneka which provides a cloud

application development platform (CAP) for developing and

running compute and data intensive applications. As a

platform it provides users with both a runtime environment for

executing applications and a set of API’s that allow you to

build new applications. PaaS also addresses scheduling of

tasks and dynamic resource provisioning. The Data Grid

Layer forms the network of data centre(s) where the satellite

data is archived and the data transfer takes place via FTP. The

Data Grid hosts satellite data received from various sensors.

SaaS layer consists of OGC [31] compliant web processing

Services (WPS) [31] for processing satellite data.

The deployed private cloud consists of a Cloud portal which is

the single point of entry for users. The cloud portal allows the

data providers and researchers to publish their data and remote

sensing applications through the cloud portal. The research

analysts can use the published data and applications for

analysis. In the following sections we discuss Infrastructure

Virtualization, Application Services, Dynamic Resource

Provisioning, Results and Conclusions.

A. Infrastructure Virtualization

The servers used to set up a scalable cloud environment have

the configuration as shown in Table I. This infrastructure can

be virtualized as 128 independent systems comprising a single

core each, which enables us to provide a large desktop

infrastructure using six servers. The IaaS layer is configured

using Xen Hypervisor, and the resources are managed using

Xen Center.

TABLE I Server Configuration

Server Quantity No. of

CPUs(Cores)

Total CPUs

(Cores)

Dell

Blade

4 24 96

HP

proliant

2 16 32

A resource pool is created to club all the resources for better

utilization and management. Templates are created so that

virtual machines can be cloned as and when required.

TABLE II Template Configuration

Template OS RAM(MB)

(MIN)

DISK(GB) (MIN)

Processor No. of Cores

Geo Win XP

Sp2

128 10 Intel Xeon 64bit,3GHz

1

Ortho Win

XP Sp2

256 10 Intel Xeon

64bit, 3GHz

1

DEM Win

XP Sp2

256 10 Intel Xeon

64bit, 3GHz

1

A Template is a self contained operating system image having

configuration settings necessary to process the demand. The

templates are configured on the local systems, tested and then

imported to the pool. Dynamic resource provisioning

mechanisms of Aneka is used to scale the virtual machines

depending on the load. The configuration of the templates is

shown in Table II.

B. Data as a Service

Data collected from various satellites is archived in data

centre(s) / ground station, which are geographically

distributed across the globe. Due to the visibility constraints of

satellites, the data transmitted by satellite sensors is received

at different ground stations which constitute the data grid as

shown in Fig. 3.

Data grid/cloud is an emerging technology for managing large

amounts of distributed data. This Technology is highly

anticipated by scientific communities, such as satellite data

processing, where research communities work with outsized

collection of data. Data in these data centre(s) is replicated

(mirrored) across various sites, and in some cases similar data

may be received at more than one centre for high availability.

Data replication technique is useful for redundancy and also in

selecting the site which can give better data transfer rate in

case of network congestion; the decision can be made based

on the site for which communication network is less

congested over the nearby proximity site. Data Grid/Cloud is

interfaced with cloud as shown in Fig 3. Data is made

available as a service to users. It allows them to view process

and analyze data without having it transferred on to their local

systems and thus eliminating all the data management issues.

Fig. 3 Data Grid/Cloud

Satellite image metadata is very significant to understand

image itself. Here we propose publishing of metadata

information in the form of XML which is validated against an

external xml schema definition. Users can make use of the

metadata publishing service to access metadata of any desired

image. Also, every image is delivered with a metadata file

which describes the image. Even though the satellite data is

acquired in various formats and resolutions, but they can be

put into a single agreed format. Here, we used the Geonetwork

[31] metadata format according to the ISO 19115 standard.

Metadata consists of various parameters like date of

acquisition, satellite, sensor, corner coordinates and cost per

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square kilometer, the structure is shown in Table III. In the

schema the information under <strip> </strip> denotes the

information about each strip (data).

Publisher: owner of the data.

Date: Date on which the data is recorded.

Satellite: The unique satellite identification number.

Sensor: Sensor by which the data is recorded (like

Panchromatic, Multispectral).

Segment: Scene identification in the corresponding orbit.

Orbit: The unique orbit path of the satellite.

File: The directory path and file to be identified

Lat#, Long#: The four corners of the recorded data.

Cost: Amount for processing per Square kilometer.

TABLE III Sample Metadata

<STRIPS>

<STRIP>

<PUBLISHER>adrin</PUBLISHER > <DATE>14-MAR-07</DATE >

<SATELLITE>IRS-P5</ SATELLITE >

<SENSOR>PAN</ SENSOR > <SEGMENT>6042332</ SEGMENT >

<ORBIT>10047</ ORBIT >

<FILE>20070314_604233.Img </FILE> <LAT1>36.106702</ LAT1 >

< LON1 >94.296127</ LON1>

< LAT2>36.048473</ CORNER2_LAT > < LON2>94.626013</ CORNER2_LON >

< LAT3 >35.780754</ CORNER3_LAT >

< LON3 >94.550916</ CORNER3_LON> < LAT4>35.83888</ CORNER4_LAT >

<LON4 >94.222143</CORNER4_LON>

<COST>5.0</ COST > </STRIP>

</STRIPS>

When data is selected from the grid, the required region of

interest only can be extracted and transferred instead of full

file. This methodology has many significant advantages for

the user and Cloud community; such as better utilization of

the network bandwidth, reducing the data transfer time, and

providing “pay for what you use” model. Currently FTP is

used for transferring files between the master and executor

nodes.

Data Query

A web interface is proposed and provided to users which

allow them to query for datasets available on the data grid.

The query results are overlaid graphically on a world map

which makes it easier for the users to select the required data.

The world map provides users the contextual information

about the location itself. The present implementation is

configured using simple Geoserver [27] with Blue marble and

world cities layer, which can be augmented/replaced by more

content rich cites like Bhuvan [32], Google maps [33].

The selected data is then overlaid as vector layer, using an in

house developed overlay tool as show in Fig. 4. For the

selected data users can place demand for Geo, Ortho or DEM

services either by marking regions of interest on the coverage

or by selecting map sheets falling within the coverage extents.

These requests are then finally submitted to the cloud for

processing.

Fig. 4 Selected coverage overlaid on the world map

C. Platform and Application Services

Application services are implemented as OGC complaint web

services. The product generation process is made available as

a cloud service to users for processing data. VAPS (Value

Added Product System) [24] is an in house customized

desktop tool which enables users to:

view and select regions of interest

select and refine ground control points (GCP) to do RSM

for better geometric accuracy [16] and

To generate GEO, ORTHO or DEM (data elevation

model) products in different scales mentioned in Table IV.

The cloud application services offer all the mentioned above

features of VAPS to users. It enables the users to view and

select the data of their choice for product generation. Each

selection can be treated as an independent task. Hence, task

based programming model of Aneka is used to execute

multiple tasks on the cloud infrastructure simultaneously.

Aneka as described previously is a cloud application

development platform that leverages scalable cloud

infrastructure for application development and execution. It

provides a runtime environment and a set of APIs for

programming in .NET environment; that allow developers to

build .NET applications leveraging cloud. Aneka, while

mostly designed to exploit the computing power of windows

based machines, which are most common within an enterprise

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environment, is portable over different platform and operating

systems by leveraging implementations like Mono. Currently,

in this work we experimented the application to run on

windows based Desktop PCs. The virtual machines are

created using Xen API’s and data transfer and scheduling is

addressed by Aneka FTP and Aneka Clock priority based

scheduling algorithms. After successful execution the data can

be downloaded to the client. Aneka comes with the various

scheduling algorithms like FIFO, Clock Rate Priority, etc. for

optimal execution of tasks on the virtual infrastructure. The

jobs submitted to the scheduler are monitored via web based

console which uses the job monitoring service. The status of

the jobs can be: un- submitted, submitted, running, finished

and failed. The description of job’s status is given below.

Un-submitted: Job submitted to the scheduler but not

scheduled to any virtual machine resource.

Submitted: Job is assigned to the virtual machine for

execution.

Running: Job is under execution

Finished: Job finished processing; ready to view and

download.

Failed: Job failed; should be rescheduled on other virtual

machine.

The existing legacy desktop based applications in C/C++ are

transformed to .NET services by leveraging PInvoke

(Platform Invocation Services) [23]. These services are

published in Aneka container [24] for data product generation.

As and when the tasks are submitted by various users, the

scheduler automatically invokes and does the service

migration to the executor nodes for computation.

TABLE IV

Scale Approximate Area(in

Km)

1 million 193600

250,000 12100

50,000 756.25

25,000 189.0625

6. Results

The model is generic and is applicable for

public/private/hybrid clouds. The private cloud proposed is

configured with the DELL Blades X64 systems with the

Xen[25] from Citrix for virtualization of the resources , Aneka

[23] for resource provisioning, and finally we present data

publishing, product generation applications as OGC (Open

Geospatial Consortium) [27] compliant Software As A

Service.

In the proposed private cloud we conduct two sets of

experiments; first experiment is to compute optimal resources

required for processing satellite data products like GEO,

ORTHO and DEM and the second compares the task

execution times between desktop processor and virtual

machines on the cloud.

A. Experiment 1: Optimal Resource Configuration

The table V below depicts the average time taken to generate

each of the data products in different RAM configurations.

The results show that systems with 512 MB RAM is sufficient

enough to generate the data products for GEO, ORTHO and

DEM. Hence, templates are created with the resources of

512MB, Windows XP operating system and 10 GB disk

storage space.

TABLE V

Type RAM(MB) Avg Time (sec)

Geocode(10 Tasks)

128 159.75

256 50.0072

512 41.7919

Ortho ( 10 Tasks)

256 211.3333

512 140.3333

DEM (10 Tasks)

256 260

512 205

Fig. 5 Times lines for Table V

The above shown experiments are conducted on different data

sets and for various sensors. The results depict, however

increase in the RAM from 512 MB to 1024 MB will not

achieve a significant improvement in reducing average time of

execution. Hence virtual resource with 512MB RAM is

configured and imported in the resource pool as template for

dynamic resource provisioning using Aneka in Xen.

B. Experiment 2: Comparison of Execution between

Desktop and a Cloud System

We conducted the experiment to compare the execution

timings between desktop processor and virtual machines in

cloud. Here we demonstrate an experiment to achieve high

throughput using cloud infrastructure. The major drawback of

this desktop application is that only one product can be

RAM

Avg Time

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generated at a time whereas in cloud we can dynamically

provision resources and each task can be scheduled on a

different virtual machine. For instance, if there is a

requirement of hundred products, in a desktop application

these hundred requests are processed one by one in a

sequential order while in cloud we can dynamically provision

hundred virtual machines and then each product request

executes simultaneously on each of the hundred virtual

machines which in turn increases throughput. Tables VI and

VII display the results obtained from this experiment.

TABLE VI Timings for geocoded product

Scale

Desktop(Single Task)

– seconds

Cloud(100 Tasks)-

seconds

One

Million

126 132

250,000 9 21

50,000 2 17

25,000 2 18

These results demonstrate the time required to execute

hundred tasks in cloud and is nearly equal to process a single

product on the desktop. With this it is evident that the

proposed private cloud is capable of handling large number of

demands to achieve high performance and high through by

leveraging compute resources effectively. In cloud

environment we can configure each core in our resource pool

as an individual system and this system would be capable of

handling each job request (product generation job).

Fig. 6 Time lines for Desktop / Cloud – geocoded Product

TABLE VII Timings for ortho product

Scale Desktop Timings(

Single Task)(sec)

Cloud

Timings(100

Tasks)(sec)

1,000,000 1540 1557

250,000 72 90

50,000 7 21

25000 6 19

Fig . 7 Time lines for Desktop / Cloud- Ortho Product

7. Conclusions

The model “High Performance Private Cloud for Satellite

Data Processing” demonstrated a Cloud Computing

Framework for Satellite Data Product Generation to achieve

high throughput/performance over a standalone bag of tasks

processing in desktop grid.

Experiment I described in results section, clearly indicated

that if optimal resource configuration for any application can

be arrived at, then one could use cloud resources efficiently

thus increasing VM density leading to better throughput,

performance and better resource utilization. Here, we

presented a work for transforming the desktop based

interactive applications as .NET services and using

dynamically scalable computing infrastructure to achieve the

high performance using the Aneka Task based programming

model and schedulers.

Effective utilization of the computing resources, load

balancing, failure job management and high throughput is

demonstrated in the experiment II, using the virtual desktop

and PaaS infrastructure.

8. Future work

The paper presents the work focused on private cloud, but

transforming it to a public cloud / hybrid cloud poses many

challenges to be explored in the areas like privacy, security,

vendor lock-in. Hybrid cloud model can be explored in the

case of a need for more compute resources. Currently a single

service provider for data is considered, it is proposed to

extend the same for multiple service providers where each

service provider may follow different cost models for data as

well as software services. Presently budgeting models for

estimating the cost for the processing of the data is not

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considered, a model can be worked out to compute the cost

based on the various parameters like delivery time of the

product, area in square kilometer, vicinity of the data product

etc.

Acknowledgement

We express our sincere thanks to Prof. Rajkumar Buyya,

University of Melbourne & CEO Manjrasoft Pty Ltd,

Australia for the support extended to us in understanding the

Aneka Programming model. We are thankful to Christian

Simone Vecchiola and Dileban karuna Moorthy, university of

Melbourne for the support in interfacing Aneka and Xen for

dynamic resource provisioning in the cloud. We express our

thanks to our colleague Sri A.Akilan, Scientist; ADRIN

helping us in interfacing the Desktop MPI applications with

.NET based Data Product Generation Service.

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